With its unique properties, blockchain has the great potential to improve security for FL in edge networks. blackSpecifically, the use of decentralized blockchain allows to eliminate the need for a central server in FL training [1]}. Instead, a shared immutable ledger is used to aggregate the global model and distribute global updates to learning clients for direct computation at devices. The decentralization of model aggregation not only mitigates the risk of single-point failures for better training reliability but also reduces the burden posed on the central server in global model aggregation, especially when edge networks have numerous clients. The learning updates are appended to immutable blocks for information exchange among clients during the training which ensures high security for training against external attacks. The replication of blocks to the entire network also allows all clients to verify and trace the training progress so as to ensure high trust and transparency for the FLchain system. Moreover, the elimination of a central server for FLchain model aggregation potentially alleviates communication costs and attract more mobile users to participate in data training based on its decentralized network topology, which in return enhances the scalability of mobile edge networks.
